Abstract: Meteorological factors are essential in the surface ozone (O₃) concentration. In order to explore the space-time changes and related factors, the surface O₃ concentration and meteorological factor data in Zhejiang Province from 2014 to 2019 are clusters analyzed using multiple linear regression and backward trajectory. The result shows that: (1) Time-space distribution of surface O₃ concentration is not uniform, and the seasonal variation is significant in terms of time. It generally shows the distribution of summer > autumn > spring > winter, with an upward trend year by year. The hourly average ozone concentration reaches a minimum of around 07:00 in spring, summer, autumn, and the whole year. After that, it shows a gradual upward trend until it reaches a peak at 15:00. The minimum ozone value in winter is about 1 hour later than in other seasons; the distribution is mainly concentrated in the northeast and northern parts of Zhejiang Province. (2) The multiple linear regression model results show a significant difference in impact factors in different season models. The impact strength of the final models in different seasons is quite different. The large-scale evaporation in spring and autumn contributes to more than 20% O₃ concentration. In comparison, the average relative humidity in summer contributes to more than 40% of O₃ concentration, and the autumn illumination time contribution exceeds 40%. The NO₂ contribution of autumn and winter exceeds 35%. The root-mean-square error (RMSE) of the multiple linear regression model, mean square absolute percentage error (MAPE), and variation interpretation (R²) in spring are 0.213, 26.45% and 0.422, respectively. In summer, they are 0.234, 30.49% and 0.359, respectively. In autumn, these figures are 0.169, 24.02% and 0.445, respectively; in winter they are 0.154, 34.14% and 0.419, respectively. The research shows that the earth's surface ozone has significant space and temporal distribution characteristics. Multiple linear regression model fitting results are significantly better in spring and autumn than in summer and winter.